Warning: This paper contains derogatory language that may be offensive to some readers. As a type of misinformation, hoaxes seek to propagate incorrect information in order to gain popularity on social media. Racial hoaxes are a particular kind of hoax that is particularly harmful since they falsely link individuals or groups to crimes or incidents because of their race. This involves identifying fabricated or misleading statements that falsely implicate racial groups in negative actions. One of these applications is the study of social media video thoughts based on comments from viewers. On the other hand, social media comments frequently incorporate many languages and are written in scripts that are not native to the user. They also rarely adhere to inflexible grammar norms. Lack of code-mixed annotated data for a Low-resource languages like Code-Mixed Hindi and English make this issue more challenging. In order to address this, we generated a racial hoax-annotated, code-mixed corpus of 2,611 YouTube comment postings in Hindi-English. We outline the method of building the corpus by annotation guidelines and assigning the binary values indicating the presence of racial hoax. The study also evaluates the performance of various deep learning models, in detecting these hoaxes. This work contributes a novel dataset for low-resource, code-mixed language contexts and establishes baseline results for racial hoax classification, providing a valuable resource for future research in multilingual misinformation detection.